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predictive estimability

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Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting

Christensen, Ronald

arXiv.org Machine LearningAug-23-2024

We consider ordinary least squares estimation and variations on least squares estimation such as penalized (regularized) least squares and spectral shrinkage estimates for problems with p > n and associated problems with prediction of new observations. After the introduction of Section 1, Section 2 examines a number of commonly used estimators for p > n. Section 3 introduces prediction with p > n. Section 4 introduces notational changes to facilitate discussion of overfitting and Section 5 illustrates the phenomenon of double descent. We conclude with some final comments.

christensen, prediction, predictive estimability, (15 more...)

arXiv.org Machine Learning

2408.13235

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Genre: Research Report (0.64)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.93)

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